Research on source location of micro-seismic event ‎based on dynamic cluster velocity model

A new velocity model based on dynamic cluster was proposed in this paper. During the process ‎of iteration, the sensors can be formed a cluster according to the velocity similitude degree. ‎Based on the assumption that the speeds from source to each sensor in the same cluster are ‎equal, the corresponding objective function was proposed to solve the source location, which ‎didn’t include the velocity parameter. It not only avoided the error from field measurement ‎and the inversion, but also appropriated for the actual situation that the speeds from every ‎source to different sensors are different. By analyzing 24 different cases, the positioning ‎accuracy based on the velocity model proposed in this paper was verified to be preferable and ‎stable, no matter the source is within the region of the sensor’s array or not. Even for the cases ‎of different velocity variation ranges, the velocity model was still reliable.

Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation

Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement. Meanwhile, federated learning (FL) addresses the data sharing problem, enabling privacy-preserved collaborative training to provide sufficient precious data. Great potential, therefore, arises with the confluence of them, known as multimodal federated learning. However, limitation lies in the predominant approaches as they often assume that each local dataset records samples from all modalities. In this paper, we aim to bridge this gap by proposing an Unimodal Training - Multimodal Prediction (UTMP) framework under the context of multimodal federated learning. We design HA-Fedformer, a novel transformer-based model that empowers unimodal training with only a unimodal dataset at the client and multimodal testing by aggregating multiple clients' knowledge for better accuracy. The key advantages are twofold. Firstly, to alleviate the impact of data non-IID, we develop an uncertainty-aware aggregation method for the local encoders with layer-wise Markov Chain Monte Carlo sampling. Secondly, to overcome the challenge of unaligned language sequence, we implement a cross-modal decoder aggregation to capture the hidden signal correlation between decoders trained by data from different modalities. Our experiments on popular sentiment analysis benchmarks, CMU-MOSI and CMU-MOSEI, demonstrate that HA-Fedformer significantly outperforms state-of-the-art multimodal models under the UTMP federated learning frameworks, with 15%-20% improvement on most attributes.Comment: 10 pages,5 figure

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller Language Models (SLMs) with less than 10 billion parameters. To address this limitation, we introduce Dialogue-guided Chain-of-Thought (DialCoT) which employs a dialogue format to generate intermediate reasoning steps, guiding the model toward the final answer. Additionally, we optimize the model's reasoning path selection using the Proximal Policy Optimization (PPO) algorithm, further enhancing its reasoning capabilities. Our method offers several advantages compared to previous approaches. Firstly, we transform the process of solving complex reasoning questions by breaking them down into a series of simpler sub-questions, significantly reducing the task difficulty and making it more suitable for SLMs. Secondly, we optimize the model's reasoning path selection through the PPO algorithm. We conduct comprehensive experiments on four arithmetic reasoning datasets, demonstrating that our method achieves significant performance improvements compared to state-of-the-art competitors.Comment: Accepted to EMNLP 202

Metagenomic next-generation sequencing shotgun for the diagnosis of infection in connective tissue diseases: A retrospective study

ObjectivePatients with connective tissue diseases (CTDs) are at high risk of infection due to various reasons. The purpose of the study was to investigate the infection diagnosis value of metagenomic next-generation sequencing (mNGS) shotgun in CTDs to guide the use of anti-infective therapy more quickly and accurately.MethodsIn this retrospective study, a total of 103 patients with CTDs admitted with suspected infection between December 2018 and September 2021 were assessed using mNGS as well as conventional microbiological tests (CMT).ResultsAmong these 103 patients, 65 were confirmed to have an infection (Group I) and 38 had no infection (Group II). mNGS reached a sensitivity of 92.31% in diagnosing pathogens in Group I. Moreover, mNGS showed good performance in identifying mixed infection. In all infection types, lung infection was the most common. mNGS also played an important role in detecting Pneumocystis jirovecii, which was associated with low CD4+ T-cell counts inextricably.ConclusionmNGS is a useful tool with outstanding diagnostic potential in identifying pathogens in patients with CTDs and conduce to provide guidance in clinical practice